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Schrödinger seeks a Retrosynthesis Researcher in Machine Learning (ML) to join us in our mission to transform the discovery of therapeutics and materials. Schrödinger has pioneered a physics-based software platform that enables discovery of high-quality, novel molecules for drug development and materials applications more rapidly and at lower cost compared to traditional methods. The software platform is used by biopharmaceutical and industrial companies, academic institutions, and government laboratories around the world. Our multidisciplinary drug discovery team also leverages the software platform to advance collaborative programs and its own pipeline of novel therapeutics to address unmet medical needs. As a member of our Machine Learning team, you’ll work at the forefront of computational chemistry and AI, contributing to high-impact research with real-world applications in small molecule drug discovery and materials science.   Who will love this job: An ML expert who has applied AI tools to chemical reaction prediction or retrosynthesis (e.g., reaction templates, template-free approaches) and understands organic synthesis and reaction mechanisms An experienced user of cheminformatics tools (e.g., RDKit, Open Babel) A proficient Python programmer who’s familiar with ML tools like Pytorch, Tensorflow, and JAX An excellent problem-solver who’s comfortable working collaboratively in a multidisciplinary research environment What you’ll do: Develop and implement AI/ML models (e.g., graph neural networks, transformer-based models) for retrosynthetic pathway prediction Apply deep learning techniques to predict reaction outcomes, optimize reaction conditions, and identify novel synthetic routes Curate and manage reaction datasets from literature, patents, and proprietary sources to train and validate predictive models Integrate retrosynthesis tools with cheminformatics platforms and molecular modeling software Collaborate with synthetic chemists to experimentally validate predicted retrosynthetic routes and optimize laboratory workflows Contribute to scholarly publications in high-impact journals and represent the research group in conferences and workshops What you should have: PhD in Chemistry, Computational Chemistry, Cheminformatics, or a related field A solid publication record that demonstrates expertise in retrosynthesis algorithms and computational chemistry We’d prefer to hire someone who has: Familiarity with chemical reaction databases (e.g., Reaxys, USPTO, Pistachio) Knowledge of computer-aided synthesis planning (CASP) tools and retrosynthetic analysis software (e.g., AiZynthFinder, ASKCOS, IBM RXN) A background in graph-based learning, attention mechanisms, and transformer architectures applied to chemical data Familiarity with reaction condition prediction and reaction yield optimization. Experience with Schrödinger Suite and LiveDesign Experience with de novo design and generative machine learning methods Experience with cloud computing and/or high-performance computing (HPC) resources Exposure to quantum chemistry (DFT) is a plus   Pay and perks: Schrödinger understands it’s people that make a company great. Because of this, we’re prepared to offer a competitive salary, equity-based compensation, and a wide range of benefits that include healthcare (with dental and vision), a 401k, pre-tax commuter benefits, a flexible work schedule, and a parental leave program. We have regular catered meals in the office, a company culture that is relaxed but engaged, and over a month of paid vacation time.  Our Office Management team also plans a myriad of fun company-wide events. New York is home to our largest office, but we have teams all over the world. Schrödinger is honored to have been included in Crain's New York Best Places to Work, BuiltIn's NYC Best Place to Work, and Newsweek's list of America